Surface approximation of Point Data using different Interpolation Techniques – A GIS approach


The same sequence of steps was repeated for Ground Water quality like EC, pH and SAR. Four ranges for all the three categories were set. For EC the range was < 750, 750-1500, 1500-2250 and >2250. For pH the ranges were <7.5, 7.5-8.5, 8.5-9.5 and > 9.5. For SAR the ranges were <2, 2-5, 5-10 and > 10.

Fig 5: shows the model constructed to get the weighted map and Fig 6: shows the year wise and weighted overlay map of GW depth in post monsoon period from 1995-2000.


Figure 5 Model designed to compute the weighted average of 5 years GW data


Figure 6 Temporal variation of depth to ground water levels

The resulting weighted grid was converted into vector format and clipped with respect to study area giving rise to overall situation of the study area in a span of six years (1995-2000). The same process was repeated for other GW parameters for both pre and post monsoon periods.

Conclusions:
On the basis of above studies and the results obtained, following concluding remarks can be made:
  • Both grid and TIN can be used for surface approximation.
  • In grid, spline is best suited for gently varying topography in contrast to IDW, which is more suitable for area with larger topographical variation.
  • TIN is computationally complex and is a two-step process as compared to grid, which is one-step process.
  • The spatial variation of results from Spline and TIN are nearly similar in the study area. Only difference being in no data area in TIN, which has been extrapolated in spline.
  • Although both grid and TIN methods produce good estimates, neither estimation process can be generalized for a particular application.
  • The choice of proper interpolation technique is highly area specific.
  • Grids are usually used more for region, small-scale applications, while TINs are used for more detailed and large-scale applications.
  • In the present study Spline interpolation results has been found to be in good agreement with the expected results.
  • Weighted overlay analysis helps in incorporating multiple data sets for various correlation studies.
  • The above study realizes the potential of GIS for various spatial analyses to get a scenario that cannot be perceived with independent primary layers or non-spatial databases and can be easily comprehended to arrive at a proper decision.
References:
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  • Doucette P et al. (2000). Exploring the capability of some GIS surface interpolators for DEM gap fill. PE & RS, 66: 881-888
  • Hutchinson, M. F. (1995). Interpolating mean rainfall using thin plate smoothing Spline. International Journal of GIS, 9: 385-404
  • (1994b). Modeling inside GIS. Part 2. Selecting and calibrating urban models using Arc/Info. . International journal of GIS, 8: 451-470
  • Mitasova et.al (1995). Modeling spatially and temporally distributed phenomena: new methods and tools for GRASS GIS. International Journal of GIS, 9: 433-446
  • Varekamp, C et al. (1996). Spatial interpolation using public domain software. PE & RS, 62: 845-854.
  • Sadahiro Y (2001). Analysis of surface changes using primitive events. International Journal of Geographical Information Science, 15: 523-538
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